A comparison of modelling the spatio-temporal pattern of disease: a case study of schistosomiasis japonica in Anhui Province, China. (10th December 2021)

• Record Type: Journal Article
• Authors: Su, Qing; Bergquist, Robert; Ke, Yongwen; Dai, Jianjun; He, Zonggui; Gao, Fenghua; Zhang, Zhijie; Hu, Yi
• Is Part Of: Transactions of the Royal Society of Tropical Medicine and Hygiene. Volume 116:Number 6(2022)
• Page Start: 555
• Page End: 563
• Publication Date: 2021-12-10
• Subjects: China -- dynamic -- schistosomiasis japonica -- spatio-temporal; Tropical medicine -- Periodicals; 616.9883
• DOI: 10.1093/trstmh/trab174
• Languages: English
• ISSNs: 0035-9203

Abstract

Abstract: The construction of spatio-temporal models can be either descriptive or dynamic. In this study we aim to evaluate the differences in model fitting between a descriptive model and a dynamic model of the transmission for intestinal schistosomiasis caused by Schistosoma japonicum in Guichi, Anhui Province, China. The parasitological data at the village level from 1991 to 2014 were obtained by cross-sectional surveys. We used the fixed rank kriging (FRK) model, a descriptive model, and the integro-differential equation (IDE) model, a dynamic model, to explore the space–time changes of schistosomiasis japonica. In both models, the average daily precipitation and the normalized difference vegetation index are significantly positively associated with schistosomiasis japonica prevalence, while the distance to water bodies, the hours of daylight and the land surface temperature at daytime were significantly negatively associated. The overall root mean square prediction error of the IDE and FRK models was 0.0035 and 0.0054, respectively, and the correlation reflected by Pearson's correlation coefficient between the predicted and observed values for the IDE model (0.71; p<0.01) was larger than that for the FRK model (0.53; p=0.02). The IDE model fits better in capturing the geographic variation of schistosomiasis japonica. Dynamic spatio-temporal models have the advantage of quantifying the process of disease transmission and may provide more accurate predictions.